Long-Short Term Memory Analysis of EEG Data Using Python for Emotion Classification

Python has made significant progress and its adaptability allows it to be utilized in nearly all fields recognized by humanity. The utilization of this technology in the field of research and development has left a distinctive impact. Advancements in brain research have occurred at a rapid pace, resulting in a continuous stream of novel results being unveiled on a daily basis. Electroencephalogram (EEG) signals refer to the electrical impulses generated by the brain in reaction to specific visual stimuli, such as positive, negative, neutral, or soothing stimuli. The utilization of Python, along with its robust modules and libraries, facilitates the efficient identification and subsequent study of human emotions, hence enabling emotion recognition. In this discussion, we will explore Long Short Term memory a machine learning (ML) models that demonstrate efficacy in extracting and conveying significant insights pertaining to the human brain and EEG signals.